A high-content imaging system (HCIS) may be used to obtain a microscopy image of a biological sample. Such image may include a number of cells against a background field. Further, the HCIS may be used to obtain a series of microscopy images of the biological sample, wherein, for example, each image is obtained using a different focus point. Such series of microscopy images may be combined to develop a three-dimensional view of the biological sample. Such series of microscopy images may also be analyzed to segment and identify a portion of each such image that is associated with a particular cell. Such portions may then be combined to form a three-dimensional view of the particular cell, analyzed further to identify organelles within the three-dimensional cell body, and/or develop three-dimensional statistics of the three-dimensional cell body and/or the organelles therein.
A researcher may want to obtain statistics of cells that are present in the microscopy image or series of microscopy images. Such statistics may include a count of how may cells of a particular cell type are present in the image, the range of sizes (e.g., dimensions, volumes and surface areas) of such cells, the mean, median and mode of the sizes of such cells, how well the cell conforms to particular shape (e.g., sphericity), and the like. Further, the images may be analyzed to identify organelles within cells identified in such images and the statistics of such organelles may also be developed. Before any such statistics can be calculated, cells in the microscopy image must be segmented from the background and also from any debris present in the microscopy image. In addition, images may be analyzed to calculate statistics of spheroids (collections of cells) and also the cells within such spheroids.
Manually identifying centers and boundaries of all cells in an image is time consuming and may lead to fatigue and error on the part of the researcher. The risk of fatigue and error is further exacerbated if the researcher has to analyze a series of images to manually identify a portion of each such image that is associated with a particular cell.
Thresholding, watershed, deformable models and graph-based formulations are the basis for the most commonly used segmentation techniques for microscopic images. Straightforward approaches such as an auto-threshold method may yield poor segmentation results due to the relatively low signal-to-noise ratio and the densely packed objects. A more sophisticated algorithm such as watershed, level sets, or graph based graph cut may produce reasonable results, but may not have throughput that is feasible for three-dimensional analysis because of the complexity of such algorithm and high demands on computational resources.